In human–robot teams, human situational awareness is the operator’s conscious knowledge of the team’s states, actions, plans and their environment. Appropriate human situational awareness is critical to successful human–robot collaboration. In human–robot teaming, it is often assumed that the best and required level of situational awareness is knowing everything at all times. This view is problematic, because what a human needs to know for optimal team performance varies given the dynamic environmental conditions, task context, and roles and capabilities of team members. We explore this topic by interviewing 16 participants with active and repeated experience in diverse human–robot teaming applications. Based on analysis of these interviews, we derive a framework explaining the dynamic nature of required situational awareness in human–robot teaming. In addition, we identify a range of factors affecting the dynamic nature of required and actual levels of situational awareness (i.e., dynamic situational awareness), types of situational awareness inefficiencies resulting from gaps between actual and required situational awareness, and their main consequences. We also reveal various strategies, initiated by humans and robots, that assist in maintaining the required situational awareness. Our findings inform the implementation of accurate estimates of dynamic situational awareness and the design of user-adaptive human–robot interfaces. Therefore, this work contributes to the future design of more collaborative and effective human–robot teams.

A human supervisor’s Situational Awareness (SA) is a critical aspect for successful Human-Robot Teaming (HRT). SA has been estimated using different techniques; however, many of those are associated with various biases, including recall and overgeneralisation biases. A key SA metric is latency, the delay between the time the robotic system requires supervisor assistance and the time the supervisor identifies that need in HRT experiments. Eye movements are increasingly used to assess SA across a range of domains, enabling objective and continuous SA assessment. However, to date, only a small number of features have been evaluated for estimating different types of SA latencies. In this paper, we investigated how two types of SA latencies (perceptual and comprehending) correlate with eye movement data collected during a remote field experiment, where a human supervisor directed a team of robots in a smart farming context. We identified 39 instances of SA latencies (13 perceptual and 26 comprehending). These instances were used to identify how a human supervisor’s SA is affected by task context, and to evaluate correlations between five eye movement features and SA latencies. Two eye movement features related to fixation duration and saccade duration demonstrated very strong correlations ($r \approx - 0.8$ and $r \approx 0.85$). Our findings can be extended to estimate the real-time likelihood of the human experiencing SA latency.

To address the goal of locomotion in very complex and difficult terrains, the authors are developing a new class of Ultralight Legged Robots. This paper presents the Multilegged Autonomous eXplorer (MAX), an ultralight, six-legged robot for traversal and exploration of challenging indoor and outdoor environments. The design of MAX emphasizes a low mass/size ratio, high locomotion efficiency, and high payload capability compared to total system mass. MAX is 2.25 m tall at full height and has a mass of approximately 60 kg, which makes it 5 to 20 times lighter than robots of comparable size. MAX is a research vehicle to explore modelling and control of Ultralight Legged Robots subject to flexing, oscillations and swaying; algorithms for gait planning and motion planning under uncertainty; and navigation planning for traversal of complex 3D terrains. This paper presents the design of MAX, provides an overview of the control system developed, summarizes results from indoor and outdoor tests, discusses system performance and outlines the challenges to be addressed next.

This paper investigates a mobile, wireless sensor/actuator network application for use in the cattle breeding industry. Our goal is to prevent fighting between bulls in on-farm breeding paddocks by autonomously applying appropriate stimuli when one bull approaches another bull. This is an important application because fighting between high-value animals such as bulls during breeding seasons causes significant financial loss to producers. Furthermore, there are significant challenges in this type of application because it requires dynamic animal state estimation, real-time actuation and efficient mobile wireless transmissions. We designed and implemented an animal state estimation algorithm based on a state-machine mechanism for each animal. Autonomous actuation is performed based on the estimated states of an animal relative to other animals. A simple, yet effective, wireless communication model has been proposed and implemented to achieve high delivery rates in mobile environments. We evaluated the performance of our design by both simulations and field experiments, which demonstrated the effectiveness of our autonomous animal control system.

This paper investigates a wireless sensor network deployment - monitoring water quality, e.g. salinity and the level of the underground water table - in a remote tropical area of northern Australia. Our goal is to collect real time water quality measurements together with the amount of water being pumped out in the area, and investigate the impacts of current irrigation practice on the environments, in particular underground water salination. This is a challenging task featuring wide geographic area coverage (mean transmission range between nodes is more than 800 meters), highly variable radio propagations, high end-to-end packet delivery rate requirements, and hostile deployment environments. We have designed, implemented and deployed a sensor network system, which has been collecting water quality and flow measurements, e.g., water flow rate and water flow ticks for over one month. The preliminary results show that sensor networks are a promising solution to deploying a sustainable irrigation system, e.g., maximizing the amount of water pumped out from an area with minimum impact on water quality.

This short-paper presents the design and experimental validation of model-based, mobile routing protocol for a delay tolerant network (DTN), where herds of animals are utilised as message ferries. We develop a novel routing protocol that utilises knowledge of the predicted behaviour of each ferry in order to choose optimal ferries for carrying messages from source to sink nodes, as well as minimise routing overhead of the network via adaptive beaconing based on current behaviour.

Agriculture accounts for a significant portion of the GDP in most developed countries. However, managing farms, particularly large-scale extensive farming systems, is hindered by lack of data and increasing shortage of labour. We have deployed a large heterogeneous sensor network on a working farm to explore sensor network applications that can address some of the issues identified above. Our network is solar powered and has been running for over 6 months. The current deployment consists of over 40 moisture sensors that provide soil moisture profiles at varying depths, weight sensors to compute the amount of food and water consumed by animals, electronic tag readers, up to 40 sensors that can be used to track animal movement (consisting of GPS, compass and accelerometers), and 20 sensor/actuators that can be used to apply different stimuli (audio, vibration and mild electric shock) to the animal. The static part of the network is designed for 24/7 operation and is linked to the Internet via a dedicated high-gain radio link, also solar powered. The initial goals of the deployment are to provide a testbed for sensor network research in programmability and data handling while also being a vital tool for scientists to study animal behavior. Our longer term aim is to create a management system that completely transforms the way farms are managed.

The paper describes some new wireless sensor hardware developed for pastoral and environmental applications. From our early experiments with Mote hardware, we were inspired to develop our devices with improved radio range, solar power capability, mechanical and electrical robustness, and with unique combinations of sensors. Here we describe the design and evolution of a small family of devices: radio/processor board; a soil moisture sensor interface; a single board multisensor unit for animal tracking experiments.

This paper describes how many of the navigation techniques developed by the robotics research community over the last decade may be applied to a class of underground mining vehicles (local haul dump (LHD) and haul trucks). We review the current state of the art in this area and conclude that there are essentially two basic methods of navigation applicable. We describe an implementation of a reactive navigation system on a 30 tonne LHD which has achieved full-speed operation at a production mine.

Programming robots by human demonstration is an effective method for task-level robot programming. Robot programming for contact tasks such as assembly, where both motion and forces need to be controlled, requires an understanding of the interaction with the environment and cannot effectively be accomplished by playing back recorded trajectories. Furthermore, since human demonstration is used to provide the motion and force trajectories, it is also important to include aspects of human movement control in the model of interaction with the environment. In this paper, we present a method of understanding and modeling contact tasks based on end-point stiffness using data obtained by human demonstration. The concept of end-point stiffness has been used both for explaining human movement control and for the control of robots. Experimental results demonstrate the effectiveness of the method in understanding and interpreting the data obtained by human demonstration of contact tasks. 1 Introd...

A new approach to skill acquisition in assembly is proposed. An assembly skill is represented by a hybrid dynamic system where a discrete event controller models the skill at the task level. The output of the discrete event controller provides the reference commands for the underlying robot controller. This structure is naturally encoded by a hidden Markov model (HMM). The HMM parameters are obtained by training on sensory data from human demonstrations of the skill. Currently, assembly tasks have to be performed by human operators or by robots using expensive fixtures. Our approach transfers the assembly skill from an expert human operator to the robot, thus making it possible for a robot to perform assembly tasks without the use of expensive fixtures.

Many robotic tasks are easily described using discrete event dynamic systems. However, the robot sensory and control systems operate in the continuous domain, leading to the problem of associating states of the continuous system with the states and events (changes in state) in the discrete task space. This paper presents a new approach to discretizing sensory data, based on discriminant functions and clustering techniques, for applications in robotic process monitoring and in interpreting human sensory data. The discriminant functions are learned from real sensory data, and hence the approach has the advantages of being adaptive, and also of taking into account various task parameters such as friction. Most importantly, the approach can be adapted quickly to different tasks by simply learning a new set of discriminant functions from sensory data corresponding to the task. Experimental results are presented to demonstrate the effectiveness of this approach.